What was all that about? Peak MOOC hype and post-MOOC legacies
Bibliographic record
Abstract
This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book sketches out the brief history of Massive Open Online Courses (MOOCs) and explores the promise that MOOCs presented and the disruptive challenge they offered to universities and university education. The first &s;official&s; MOOC came from the University of Manitoba in 2009, however, a more commonly cited starting point for this type of online presence is 2011 and, more specifically, Sebastian Thrun&s;s Artificial Intelligence MOOC and Daphne Koller and Andrew Ng&s;s course on Machine Learning. The book explores how universities are using MOOCs to engage with both global and local learners to provide &s;their own voice and agendas&s;. The book works through the process of the development of the Disability and a Good Life MOOC series at the University of New South Wales in 2016. The book provides a reflection on disability and accessibility in higher education in the MOOC context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".